# A tibble: 3,038 × 5
location_name Province District `malaria_rdt_0-4` `malaria_rdt_5-14`
<chr> <chr> <chr> <int> <int>
1 Facility 1 North Spring 11 12
2 Facility 2 North Bolo 11 10
3 Facility 3 North Dingo 8 5
4 Facility 4 North Bolo 16 16
5 Facility 5 North Bolo 9 2
6 Facility 6 North Dingo 3 1
7 Facility 6 North Dingo 4 0
8 Facility 5 North Bolo 15 14
9 Facility 5 North Bolo 11 11
10 Facility 5 North Bolo 19 15
# ℹ 3,028 more rows
pivot_longer(data = , # dataframecols = , # columnas a pivotearnames_to = , # nombre de la nueva variable,values_to = ) # variable donde queda los valores
# A tibble: 12,152 × 6
location_name Province District newid gr_edad casos
<chr> <chr> <chr> <int> <chr> <int>
1 Facility 1 North Spring 1 malaria_rdt_0-4 11
2 Facility 1 North Spring 1 malaria_rdt_5-14 12
3 Facility 1 North Spring 1 malaria_rdt_15 23
4 Facility 1 North Spring 1 malaria_tot 46
5 Facility 2 North Bolo 2 malaria_rdt_0-4 11
6 Facility 2 North Bolo 2 malaria_rdt_5-14 10
7 Facility 2 North Bolo 2 malaria_rdt_15 5
8 Facility 2 North Bolo 2 malaria_tot 26
9 Facility 3 North Dingo 3 malaria_rdt_0-4 8
10 Facility 3 North Dingo 3 malaria_rdt_5-14 5
# ℹ 12,142 more rows
pivot_wider()
Fuente: Neal Batra. EpiRhandbook
pivot_wider
Reto: Crear la tabla a partir de los datos de Malaria
Luego si podemos utilizar la siguiente función que convertira en una tabla ancha (wider)
pivot_wider(data = , # dataset a pivotearid_cols =, #columan que quedara fija names_from =# variable que sus valores se convertiran en columnas,values_from = ) # los valores que alimentaras la nuevas columnas